Estimation of density-dependent mortality of juvenile bivalves in the Wadden Sea.

Andresen H, Strasser M, van der Meer J - PLoS ONE (2014)

Bottom Line:
We analyzed count data from three years of temporal sampling during the first months after bivalve settlement at ten transects in the Sylt-Rømø-Bay in the northern German Wadden Sea.Measurement error was estimated by bootstrapping, and residual deviances were adjusted by adding process error.With simulations the effect of these two types of error on the estimate of the density-dependent mortality coefficient was investigated.

ABSTRACTWe investigated density-dependent mortality within the early months of life of the bivalves Macoma balthica (Baltic tellin) and Cerastoderma edule (common cockle) in the Wadden Sea. Mortality is thought to be density-dependent in juvenile bivalves, because there is no proportional relationship between the size of the reproductive adult stocks and the numbers of recruits for both species. It is not known however, when exactly density dependence in the pre-recruitment phase occurs and how prevalent it is. The magnitude of recruitment determines year class strength in bivalves. Thus, understanding pre-recruit mortality will improve the understanding of population dynamics. We analyzed count data from three years of temporal sampling during the first months after bivalve settlement at ten transects in the Sylt-Rømø-Bay in the northern German Wadden Sea. Analyses of density dependence are sensitive to bias through measurement error. Measurement error was estimated by bootstrapping, and residual deviances were adjusted by adding process error. With simulations the effect of these two types of error on the estimate of the density-dependent mortality coefficient was investigated. In three out of eight time intervals density dependence was detected for M. balthica, and in zero out of six time intervals for C. edule. Biological or environmental stochastic processes dominated over density dependence at the investigated scale.

pone-0102491-g003: Simulation results for Macoma balthica in the time intervals of density decline.Dashed line: slope value of the original regression through the observed data. White line: average slope values resulting when measurement error is added to deterministic data on preset regression lines. Grey area: corresponding 95% confidence intervals. Black dots: average slope values resulting when process error and measurement error are added to deterministic data on preset regression lines. Whiskers: corresponding 95% confidence intervals. When the lower end of the confidence interval for the preset slope 1 ends above the observed slope value, the observed slope differs significantly from 1 and density dependence is concluded (arrows).

Mentions:
Three incidences of density dependence out of eight investigated cases were found for Macoma balthica, one of them in the middle of the summer 1996 and another two in 1998 in the time intervals in the middle and at the end of the summer (Fig. 3 A, G and H). Density dependence was concluded because the lower end of the modelled confidence interval for the preset slope 1 (no density dependence) ends above the observed slope value (arrows in Fig. 3), that means the observed slope differs significantly from 1. In Cerastoderma edule this was never the case (Fig. 4). The time intervals with density-dependent mortality did not start with an especially high density or large range of densities, nor were the mortalities exceptional (Fig. 5).

pone-0102491-g003: Simulation results for Macoma balthica in the time intervals of density decline.Dashed line: slope value of the original regression through the observed data. White line: average slope values resulting when measurement error is added to deterministic data on preset regression lines. Grey area: corresponding 95% confidence intervals. Black dots: average slope values resulting when process error and measurement error are added to deterministic data on preset regression lines. Whiskers: corresponding 95% confidence intervals. When the lower end of the confidence interval for the preset slope 1 ends above the observed slope value, the observed slope differs significantly from 1 and density dependence is concluded (arrows).

Mentions:
Three incidences of density dependence out of eight investigated cases were found for Macoma balthica, one of them in the middle of the summer 1996 and another two in 1998 in the time intervals in the middle and at the end of the summer (Fig. 3 A, G and H). Density dependence was concluded because the lower end of the modelled confidence interval for the preset slope 1 (no density dependence) ends above the observed slope value (arrows in Fig. 3), that means the observed slope differs significantly from 1. In Cerastoderma edule this was never the case (Fig. 4). The time intervals with density-dependent mortality did not start with an especially high density or large range of densities, nor were the mortalities exceptional (Fig. 5).

Bottom Line:
We analyzed count data from three years of temporal sampling during the first months after bivalve settlement at ten transects in the Sylt-Rømø-Bay in the northern German Wadden Sea.Measurement error was estimated by bootstrapping, and residual deviances were adjusted by adding process error.With simulations the effect of these two types of error on the estimate of the density-dependent mortality coefficient was investigated.

ABSTRACTWe investigated density-dependent mortality within the early months of life of the bivalves Macoma balthica (Baltic tellin) and Cerastoderma edule (common cockle) in the Wadden Sea. Mortality is thought to be density-dependent in juvenile bivalves, because there is no proportional relationship between the size of the reproductive adult stocks and the numbers of recruits for both species. It is not known however, when exactly density dependence in the pre-recruitment phase occurs and how prevalent it is. The magnitude of recruitment determines year class strength in bivalves. Thus, understanding pre-recruit mortality will improve the understanding of population dynamics. We analyzed count data from three years of temporal sampling during the first months after bivalve settlement at ten transects in the Sylt-Rømø-Bay in the northern German Wadden Sea. Analyses of density dependence are sensitive to bias through measurement error. Measurement error was estimated by bootstrapping, and residual deviances were adjusted by adding process error. With simulations the effect of these two types of error on the estimate of the density-dependent mortality coefficient was investigated. In three out of eight time intervals density dependence was detected for M. balthica, and in zero out of six time intervals for C. edule. Biological or environmental stochastic processes dominated over density dependence at the investigated scale.